Monthly Archives: August 2011

We’ve just completed our very first trial run of the Standby Task Volunteer Force (SBTF) Satellite Team. As mentioned in this blog post last week, the UN approached us a couple weeks ago to explore whether basic satellite imagery analysis for Somalia could be crowdsourced using a distributed mechanical turk approach. I had actually floated the idea in this blog post during the floods in Pakistan a year earlier. In any case, a colleague at Digital Globe (DG) read my post on Somalia and said: “Lets do it.”

So I reached out to Luke Barrington at Tomnod to set up distributed micro-tasking platform for Somalia. To learn more about Tomond’s neat technology, see this previous blog post. Within just a few days we had high resolution satellite imagery from DG and a dedicated crowdsourcing platform for imagery analysis, courtesy of Tomnod . All that was missing were some willing and able “mapsters” from the SBTF to tag the location of shelters in this imagery. So I sent out an email to the group and some 50 mapsters signed up within 48 hours. We ran our pilot from August 26th to August 30th. The idea here was to see what would go wrong (and right!) and thus learn as much as we could before doing this for real in the coming weeks.

It is worth emphasizing that the purpose of this trial run (and entire exercise) is not to replicate the kind of advanced and highly-skilled satellite imagery analysis that professionals already carry out. This is not just about Somalia over the next few weeks and months. This is about Libya, Syria, Yemen, Afghanistan, Iraq, Pakistan, North Korea, Zimbabwe, Burma, etc. Professional satellite imagery experts who have plenty of time to volunteer their skills are far and few between. Meanwhile, a staggering amount of new satellite imagery is produced every day; millions of square kilometers’ worth according to one knowledgeable colleague.

This is a big data problem that needs mass human intervention until the software can catch up. Moreover, crowdsourcing has proven to be a workable solution in many other projects and sectors. The “crowd” can indeed scan vast volumes of satellite imagery data and tag features of interest. A number of these crowds-ourcing platforms also have built-in quality assurance mechanisms that take into account the reliability of the taggers and tags. Tomnod’s CrowdRank algorithm, for example, only validates imagery analysis if a certain number of users have tagged the same image in exactly the same way. In our case, only shelters that get tagged identically by three SBTF mapsters get their locations sent to experts for review. The point here is not to replace the experts but to take some of the easier (but time-consuming) tasks off their shoulders so they can focus on applying their skill set to the harder stuff vis-a-vis imagery interpretation and analysis.

The purpose of this initial trial run was simply to give SBTF mapsters the chance to test drive the Tomnod platform and to provide feeback both on the technology and the work flows we put together. They were asked to tag a specific type of shelter in the imagery they received via the web-based Tomnod platform:

There’s much that we would do differently in the future but that was exactly the point of the trial run. We had hoped to receive a “crash course” in satellite imagery analysis from the Satellite Sentinel Project (SSP) team but our colleagues had hardly slept in days because of some very important analysis they were doing on the Sudan. So we did the best we could on our own. We do have several satellite imagery experts on the SBTF team though, so their input throughout the process was very helpful.

Our entire work flow along with comments and feedback on the trial run is available in this open and editable Google Doc. You’ll note the pages (and pages) of comments, questions and answers. This is gold and the entire point of the trial run. We definitely welcome additional feedback on our approach from anyone with experience in satellite imagery interpretation and analysis.

The result? SBTF mapsters analyzed a whopping 3,700+ individual images and tagged more than 9,400 shelters in the green-shaded area below. Known as the “Afgooye corridor,” this area marks the road between Mogadishu and Afgooye which, due to displacement from war and famine in the past year, has become one of the largest urban areas in Somalia. [Note, all screen shots come from Tomnod].

Last year, UNHCR used “satellite imaging both to estimate how many people are living there, and to give the corridor a concrete reality. The images of the camps have led the UN’s refugee agency to estimate that the number of people living in the Afgooye Corridor is a staggering 410,000. Previous estimates, in September 2009, had put the number at 366,000″ (1).

Thanks to Tomnod’s CrowdRank algorithm, we were able to analyze consensus between mapsters and pull out the triangulated shelter locations. In total, we get 1,423 confirmed locations for the types of shelters described in our work flows. A first cursory glance at a handful (“random sample”) of these confirmed locations indicate they are spot on. As a next step, we could crowdsource (or SBTF-source, rather) the analysis of just these 1,423 images to triple check consensus. Incidentally, these 1,423 locations could easily be added to Google Earth or a password-protected Ushahidi map.

We’ve learned a lot during this trial run and Luke got really good feedback on how to improve their platform moving forward. The data collected should also help us provide targeted feedback to SBTF mapsters in the coming days so they can further refine their skills. On my end, I should have been a lot more specific and detailed on exactly what types of shelters qualified for tagging. As the Q&A section on the Google Doc shows, many mapsters weren’t exactly sure at first because my original guidelines were simply too vague. So moving forward, it’s clear that we’ll need a far more detailed “code book” with many more examples of the features to look for along with features that do not qualify. A colleague of mine suggested that we set up an interactive, online quiz that takes volunteers through a series of examples of what to tag and not to tag. Only when a volunteer answers all questions correctly do they move on to live tagging. I have no doubt whatsoever that this would significantly increase consensus in subsequent imagery analysis.

Please note: the analysis carried out in this trial run is not for humanitarian organizations or to improve situational awareness, it is simply for testing purposes only. The point was to try something new and in the process work out the kinks so when the UN is ready to provide us with official dedicated tasks we don’t have to scramble and climb the steep learning curve there and then.

In related news, the Humanitarian Open Street Map Team (HOT) provided SBTF mapsters with an introductory course on the OSM platform this past weekend. The HOT team has been working hard since the response to Haiti to develop an OSM Tasking Server that would allow them to micro-task the tracing of satellite imagery. They demo’d the platform to me last week and I’m very excited about this new tool in the OSM ecosystem. As soon as the system is ready for prime time, I’ll get access to the backend again and will write up a blog post specifically on the Tasking Server.

I was in Kansas last week for TEDxKC. The venue for the event was spectacular: The Nelson-Atkins Museum of Art. The curator kept the museum open that evening for participants to enjoy after the talks. I relished the tranquility and found myself lost in thought in front of a quiet masterpiece by Francois Boucher. I had shared my story “Changing the World, One Map at a Time” on the TEDx stage earlier that evening and realized that live maps and museums weren’t that different. Both are curated and display moments in history, the good and bad.

The opening speaker of TEDxKC 2011 was Jenn Lim, the CEO and Chief Happiness Officer of Delivering Happiness, a company she co-founded to inspire happiness in work, community and everyday life. I found Jenn during the reception and asked: “How about crowdsourcing happiness and creating a happiness map?” The thought had come to me just minutes before my talk. I’ve been focusing on crisis mapping for a while but there’s obviously so much more to live maps.

Historian Geoffrey Blainey argues that “for every thousand pages on the causes of war, there is less than one page on the causes of peace.” And yet, peace is far more pervasive than war, we simply don’t write about it. The same is true of things that go well in general. So what if we made the good stuff more visible and showed just how much more frequent and pervasive peace and happiness are then we may at first realize?

Current world happiness maps are computed by academics using various structural indicators and macro-level statistics. These maps are limited to the nation-state level of analysis which suggests that everyone in a given country is equally happy throughout an entire year. Maps don’t get more old school than this. What is blatantly missing is something like Gross National Happiness (GNH) data but disaggregated, user-generated and mapped in real-time.

I had pitched the same idea to Coca-Cola two years ago as part of their Expedition 206 campaign. Three “Happiness Ambassadors” travelled to 206 countries in 2010 to find what happiness means to the world. I had heard about the project through a good friend who had auditioned to be one of the Happiness Ambassadors.

The idea of a happiness world tour appealed to me a lot but why not let people speak for themselves and map what happiness means to them? The Expedition 206 Team was already using social media and a map as part of their campaign, so a crowdsourced happiness world map made perfect sense.

This is precisely what I pitched to Coca-Cola as the screenshot below shows. My colleague and friend Caleb Bell from Ushahidi did some awesome interface design work for the pitch.

While Coca-Cola was intrigued by the idea, they had already launched their Expedition 206 Social Media strategy. In any case, this project came to mind just minutes before I got on the TEDxKC stage last week and it’s something I’d like to take up again and would love some help on.

We could customize the Ushahidi platform and smart phone apps. People could then share what happiness means to them by “checking in” with a status update and/or a picture. The content could then be automatically mapped on a World Happiness Map.

Happiness badges could also be won when people check into certain places and/or with certain updates. Happiness messages or pictures could be embedded across the map (geo-fencing) so that anyone checking in at any given time place/time would receive a message/picture that would make them smile.

One could also “Subscribe to Happiness!” by allowing people to receive any happiness updates/pictures from people around them. For example, Mike could subscribe to happiness updates say within a 5 mile radius of the Nelson-Atkins Art Museum. When Kelly checks in on her way to the museum, Mike would get an update with the happiness message (either anonymous or with Kelly’s name/picture).

I think this could be quite a powerful campaign, especially given the state of the world economy and ongoing crises. Incidentally, smiling has been scientifically shown to have positive health effects such as extending lifespans, as my colleague Ron Gutman points out in this TED talk.

A crowdsourcing happiness campaign would help remind people about what they do have and what they can be grateful for. One idea, then, might be to launch this campaign as part of the upcoming Thanksgiving holidays. I’d love to partner with someone to make this happen. So please get in touch if you’d like to help. In the meantime, smile! : )

My colleague Robert Soden was absolutely right: Tomnod is definitely iRevolution material. This is why I reached out to the group a few days ago to explore the possibility of using their technology to crowdsource the analysis of satellite imagery for Somalia. You can read more about that project here. In this blog post, however, is to highlight the amazing work they’ve been doing with National Geographic in search of Genghis Khan’s tomb.

This “Valley of the Khans Project” represents a new approach to archeology. Together with National Geographic, Tomnod has collected thousands of GeoEye satellite images of the valley and designed a simple user interface to crowdsource the tagging of roads, rivers and modern or ancient structures they. I signed up to give it a whirl and it was a lot of fun. A short video gives a quick guide on how to recognize different structures and then off you go!

You are assigned the rank “In Training” when you first begin. Once you’ve tagged your first 10 images, you progress to the next rank, which is “Novice 1″. The squares at the bottom left represent the number of individual satellite images you’ve tagged and how many are left. This is a neat game-like console and I wonder if there’s a scoreboard with names, listed ranks and images tagged.

In any case, a National Geographic team in Mongolia use the results to identify the most promising archeological sites. The field team also used Unmanned Areal Vehicles (UAVs) to supplement the satellite imagery analysis. You can learn more about the “Valley of the Khans Project” from this TEDx talk by Tomnod’s Albert Lin. Incidentally, Tomnod also offered their technology to map the damage from the devastating earthquake in New Zealand, earlier this year. But the next project I want to highlight focuses on the forests of Borneo.

I literally just found out about the “EarthWatchers: Planet Patrol” project thanks to Edwin Wisse’s comment on my previous blog post. As Edwin noted, EarthWatchers is indeed very similar to the Somalia initiative I blogged about. The project is “developing the (web)tools for students all over the world to monitor rainforests using updated satellite imagery to provide real time intelligence required to halt illegal deforestation.”

This is a really neat project and I’ve just signed up to participate. EarthWatchers has designed a free and open source platform to make it easy for students to volunteer. When you log into the platform, EarthWatchers gives you a hexagon-shaped area of the Borneo rainforest to monitor and protect using the satellite imagery displayed on the interface.

The platform also provides students with a number of contextual layers, such as road and river networks, to add context to the satellite imagery and create heat-maps of the most vulnerable areas. Forests near roads are more threatened since the logs are easier to transport, for example. In addition, volunteers can compare before-and-after images of their hexagon to better identify any changes. If you detect any worrying changes in your hexagon, you can create an alert that notifies all your friends and neighbors.

An especially neat feature about the interface is that it allows students to network online. For example, you can see who your neighbors in nearby hexagons are and even chat with them thanks to a native chat feature. This is neat because it facilitates collaboration mapping in real time and means you don’t feel alone or isolated as a volunteer. The chat feature helps to builds community.

If you’d like to learn more about this project, I recommend the presentation below by Eduardo Dias.

The third and final project I want to highlight is called Galaxy Zoo. I first came across this awesome example of citizen science in MacroWikinomics—an excellent book written by Don Tapscott and Anthony Williams. The purpose of Galaxy Zoo is to crowdsource the tagging and thus classification of galaxies as either spiral or elliptical. In order to participate, users to take a short tutorial on the basics of galaxy morphology.

While this project began as an experiment of sorts, the initiative is thriving with more than 275,000 users participating and 75 million classifications made. In addition, the data generated has resulted in several peer reviewed publica-tions real scientific discoveries. While the project uses imagery of the stars rather than earth, it really qualifies as a major success story in crowdsourcing the analysis of imagery.

Know of other intriguing applications of crowdsourcing for imagery analysis? If so, please do share in the comments section below.

A “geo-fence” is a virtual perimeter for a real-world geographic area—a virtually fenced off geographic location. Geo-fences can take any shape. They can also be user-generated thanks to custom-digitized geo-fencing options. Combine geo-fencing with a dynamic alerts feature and you’ve got yourself the next evolution of live mapping technologies for crisis mapping. Indeed, the ability to customize automated geo-fenced alerts is going to revolutionize the way we use crisis maps.

Several live mapping technologies like Ushahidi already have a very basic geo-fenced alerts feature. Lets say I am interested in the town of Dhuusamareeb in Somalia. I can point my cursor to that location, specify the radius of interest around this point and then subscribe to receive email and/or SMS updates for any new reports that get mapped within this area. If I’m particularly interested in food security, then I can instead subscribe to receive alerts only when reports tagged with the category “food security” is mapped within this area. This allows end users to define for themselves the type of granular information they need—a demand-based solution as opposed to a supply-side (spam-side?) solution.

But this feature is old news. There’s only so much use one can get from a simple subscribe-to-alerts feature. We need to bring more spatial and temporal geo-fencing solutions to crisis mapping. For example, I should be able to geo-fence different parts of a refugee camp and customize the automated alerts feature such that a 10% increase over a 24-hour period in the number of reports tagged with certain categories and geo-located within specified geo-fences sends an email and/or SMS to the appropriate teams in charge.

The variables that ought to be customizable by the user include the individual geo-fences, the time period over which a percentage change threshold is specified (eg., 1 hour, 4 hours, 24 hours, 2 days, 1 week etc.), the actual percentage change (eg., 5%, 20%, 80% etc) and the type of categories (eg., food security, health access, etc). In addition to percentages, users should be able to specify basic report counts, e.g., notify me when at least 20 reports have been mapped within this section of the refugee camp.

This kind of automated, customized geo-fencing threshold alerts feature has many, many applications, from public health, to conflict early warning, to disaster response, etc. Combining this type of geo-fenced alerts feature with check-in’s will make crisis mapping a lot more compelling for decision support. One could customize specific actions depending on where/when check-in’s take place and any additional content (eg., status update) included in the check-in. See my previous blog post on “Check-In’s with a Purpose: Applications for Disaster Response.”

These actions could include emails, SMS, twitter alerts, etc., to specific individuals with pre-determined content based on the parameters of a check-in. Actions could also be “physical” actions, not just information communication. For example, a certain type of customized geo-fenced alert, depending on where/when it happens, could execute a program that turns on a water pump, changes the temperature of a fridge storing vaccines, etc.

We need to make live mapping technologies more relevant for real-time decision support and I believe that geo-fencing is an important step to that affect.

p.s. Naturally, GIGO (garbage-in, garbage-out) still applies. That is, if the data is of poor quality or not existant, adding automated geo-fencing alerts is not going to improve decision-making. But that’s the case for any data processing feature. So my colleague Brian Herbert and I are in the process of adding geo-fenced alerts features to the Ushahidi platform.

You gotta love Twitter. Just two hours after I tweeted the above—in reference to this project—a colleague of mine from the UN who just got back from the Horn of Africa called me up: “Saw your tweet, what’s going on?” The last thing I wanted to was talk about the über frustrating day I’d just had. So he said, “Hey, listen, I’ve got an idea.” He reminded me of this blog post I had written a year ago on “Crowdsourcing the Analysis of Satellite for Disaster Response” and said, “Why not try this for Somalia? We could definitely use that kind of information.” I quickly forgot about my frustrating day.

Here’s the plan. He talks to UNOSAT and Google about acquiring high-resolution satellite imagery for those geographic areas for which they need more information on. A colleague of mine in San Diego just launched his own company to develop mechanical turk & micro tasking solutions for disaster response. He takes this satellite imagery and cuts it into say 50×50 kilometers square images for micro-tasking purposes.

We then develop a web-based interface where volunteers from the Standby Volunteer Task Force (SBTF) sign in and get one high resolution 50×50 km image displayed to them at a time. For each image, they answer the question: “Are there any human shelters discernible in this picture? [Yes/No].” If yes, what would you approximate the population of that shelter to be? [1-20; 21-50; 50-100; 100+].” Additional questions could be added. Note that we’d provide them with guidelines on how to identify human shelters and estimate population figures.

No shelters discernible in this image

Each 50×50 image would get rated by at least 3 volunteers for data triangulation and quality assurance purposes. That is, if 3 volunteers each tag an image as depicting a shelter (or more than one shelter) and each of the 3 volunteers approximate the same population range, then that image would get automatically pushed to an Ushahidi map, automatically turned into a geo-tagged incident report and automatically categorized by the population estimate. One could then filter by population range on the Ushahidi map and click on those reports to see the actual image.

If satellite imagery licensing is an issue, then said images need not be pushed to the Ushahidi map. Only the report including the location of where a shelter has been spotted would be mapped along with the associated population estimate. The satellite imagery would never be released in full, only small bits and pieces of that imagery would be shared with a trusted network of SBTF volunteers. In other words, the 50×50 images could not be reconstituted and patched together because volunteers would not get contiguous 50×50 images. Moreover, volunteers would sign a code of conduct whereby they pledge not to share any of the imagery with anyone else. Because we track which volunteers see which 50×50 images, we could easily trace any leaked 50×50 image back to the volunteer responsible.

Note that for security reasons, we could make the Ushahidi map password protected and have a public version of the map with very limited spatial resolution so that the location of individual shelters would not be discernible.

I’d love to get feedback on this idea from iRevolution readers, so if you have thoughts (including constructive criticisms), please do share in the comments section below.

Synchronized action is a powerful form of resistance against repressive regimes. Even if the action itself is harmless, like walking, meditation or worship, the public synchrony of that action by a number of individuals can threaten an authoritarian state. To be sure, synchronized public action demonstrates independency which may undermine state propaganda, reverse information cascades and thus the shared perception that the regime is both in control and unchallenged.

This is especially true if the numbers participating in synchrony reaches a tipping point. As Karl Marx writes in Das Kapital, “Merely quantitative differences, beyond a certain point, pass into qualitative changes.” We call this “emergent behavior” or “phase transitions” in the field of complexity science. Take a simple example from the physical world: the heating of water. A one degree increase in temperature is a quantitative change. But keep adding one degree and you’ll soon reach the boiling point of water and surprise! A physical phase transition occurs: liquid turns into gas.

In social systems, information creates friction and heat. Moreover, today’s information and communication technologies (ICTs) are perhaps the most revolutionary synchronizing tools for “creating heat” because of their scalability. Indeed, ICTs today can synchronize communities in ways that were unimaginable just a few short years ago. As one Egyptian activist proclaimed shortly before the fall of Mubarak, “We use Facebook to scheduled our protests, Twitter to coordinate, and YouTube to tell the world.” The heat is already on.

Synchrony requires that individuals be connected in order to synchronize. Well guess what? ICTs are mass, real-time connection technologies. There is conse-quently little doubt in my mind that “the advent and power of connection technologies—tools that connect people to vast amounts of information and to one another—will make the twenty-first century all about surprises;” surprises that take the form of “social phase transitions” (Schmidt and Cohen 2011). Indeed, ICTs can dramatically increase the number of synchronized participants while sharply reducing the time it takes to reach the social boiling point. Some refer to this as “punctuated equilibria” or “reversed information cascades” in various academic literatures. Moreover, this can all happen significantly faster than ever before, and as argued in this previous blog post on digital activism, faster is indeed different.

Clay Shirky argues that “this basic hypothesis is an updated version of that outlined by Jürgen Habermas in his 1962 publication, The Structural Transformation of the Public Sphere: an Inquiry into a Category of Bourgeois Society. A group of people, so Habermas’s theory goes, who take on the tools of open expression becomes a public, and the presence of a synchronized public increasingly constrains undemocratic rulers while expanding the rights of that public […].” But to understand the inherent power of synchrony and then leverage it, we must first recognized that synchrony is a fundamental force of nature that goes well beyond social systems.

In his TED Talk from 2004, American mathematician Steven Strogatz argues that synchrony may be one of the most pervasive drivers in all of nature, extending from the subatomic scale to the farthest reaches of the cosmos. In many ways, this deep tendency towards spontaneous order is what pushes back against the second law of thermodynamics, otherwise known as entropy.

Strogatz shares example from nature and shows a beautiful ballet of hundreds of birds flocking in unison. He explains that this display of synchrony has to do with defense. “When you’re small and vulnerable […] it helps to swarm to avoid and/or confuse predators.” When a predator strikes, however, all bets are off, and everyone disperses—but only temporarily. “The law of attraction,” says Strogatz, brings them right back together in synchrony within seconds. “There’s this constant splitting and reforming,” grouping and dispersion—swarming—which has several advantages. If you’re in a swarm, the odds of getting caught are far lower. There are also many eyes to spot the danger.

What’s spectacular about these ballets is how quickly they phase from one shape to another, dispersing and regrouping almost instantaneously even across vast distances. Individual changes in altitude, speed and direction are communicated and acted on across half-a-kilometer within just seconds. The same is true of fireflies in Borneo that synchronize their blinking across large distances along the river banks. Thousands and thousands of fireflies somehow overcoming the communication delay between the one firefly at one end of the bank and the other firefly at the furthest opposite end. How is this possible? The answer to this question may perhaps provide insights for social synchrony in the context of resistance against repressive regimes.

Strogatz and Duncan Watts eventually discovered the answer, which they published in their seminal paper entitled “Collective dynamics of small-world networks.” Published in the prestigious journal Nature, the paper became the most highly cited article about networks for 10 years and the sixth most cited paper in all of physics. A small-world network is a type of network in which even though most nodes are not neighbors of one another, most can still be reached from other nodes by a small number of hops or steps. In the context of social systems, this type of network results in the “small world phenomenon of strangers being linked by a mutual acquaintance.”

These types of networks often arise out of preferential attachment, an inherently social dynamic. Indeed, small world networks pervade social systems. So what does this mean for synchrony as applied to civil resistance? Are smart-mobs synonymous with synchronized mobs? Do ICTs increase the prevalence of small world networks in social systems—thus increasing robustness and co-synchrony between social networks. Will meshed-communication technologies and features like check-in’s alter the topology of small world networks?

Examples of synchrony from nature clearly show that real-time communication and action across large distances don’t require mobile phones. Does that mean the same is possible in social systems? Is it possible to disseminate information instantaneously within a large crowd without using communication technologies? Is strategic synchrony possible in this sense? Can social networks engage in instantaneous dispersion and cohesion tactics to confuse the repressive regime and remain safe?

I recently spoke with a colleague who is one of the world’s leading experts on civil resistance, and was astonished when she mentioned (without my prompting) that many of the tactics around civil resistance have to do with synchronizing cohesion and dispersion. On a different note, some physicists argue that small world networks are more robust to perturbations than other network structures. Indeed, the small work structure may represent an evolutionary advantage.

But how are authoritarian networks structured? Are they too of the small world variety? If not, how do they compare in terms of robustness, flexibility and speed? In many ways, state repression is a form of synchrony itself—so is genocide. Synchrony is clearly not always a good thing. How is synchrony best interrupted or sabotaged? What kind of interference strategies are effective in this context?

I recently caught up with a colleague at the World Bank and learned that “resilience” is set to be the new “buzz word” in the international development community. I think this is very good news. Yes, discourse does matter. A single word can alter the way we frame problems. They can lead to new conceptual frameworks that inform the design and implementation of development projects and disaster risk reduction strategies.

The term resilience is important because it focuses not on us, the development and disaster community, but rather on local at-risk communities. The terms “vulnerability” and “fragility” were used in past discourse but they focus on the negative and seem to invoke the need for external protection, overlooking the possibility that local coping mechanisms do exist. From the perspective of this top-down approach, international organizations are the rescuers and aid does not arrive until they arrive.

Resilience, in contrast, implies radical self-sufficiency, and self-sufficien-cy suggests a degree of autonomy; self-dependence rather than dependence on an external entity that may or may not arrive, that may or may not be effective, and that may or may not stay the course. In the field of ecology, the term resilience is defined as “the capacity of an ecosystem to respond to a perturbation or disturbance by resisting damage and recovering quickly.” There are thus at least two ways for “social ecosystems” to be resilient:

Resist damage by absorbing and dampening the perturbation.

Recover quickly by bouncing back.

So how does a society resist damage from a disaster? As noted in an earlier blog post, “Disaster Theory for Techies“, there is no such thing as a “natural disaster”. There are natural hazards and there are social systems. If social systems are not sufficiently resilient to absorb the impact of a natural hazard such as an earthquake, then disaster unfolds. In other words, hazards are exogenous while disasters are the result of endogenous political, economic, social and cultural processes. Indeed, “it is generally accepted among environmental geographers that there is no such thing as a natural disaster. In every phase and aspect of a disaster—causes, vulnerability, preparedness, results and response, and reconstruction—the contours of disaster and the difference between who lives and dies is to a greater or lesser extent a social calculus” (Smith 2006).

So how do we take this understanding of disasters and apply it to building more resilient communities? Focusing on people-centered early warning systems is one way to do this. In 2006, the UN’s International Strategy for Disaster Reduction (ISDR) recognized that top-down early warning systems for disaster response were increasingly ineffective. They therefore called for a more bottom-up approach in the form of people-centered early warning systems. The UN ISDR’s Global Survey of Early Warning Systems (PDF), defines the purpose of people-centered early warning systems as follows:

“… to empower individuals and communities threatened by hazards to act in sufficient time and in an appropriate manner so as to reduce the possibility of personal injury, loss of life, damage to property and the environment, and loss of livelihoods.”

Information plays a central role here. Acting in sufficient time requires having timely information about (1) the hazard(s) and (2) how to respond. As some scholars have argued, a disaster is first of all “a crisis in communicating within a community—that is, a difficulty for someone to get informed and to inform other people” (Gilbert 1998). Improving ways for local communities to communicate internally is thus an important part of building more resilient societies. This is where information and communication technologies (ICTs) play an important role. Free and open source software like Ushahidi can also be used (the subject of a future blog post).

Open data is equally important. Local communities need to access data that will enable them to make more effective decisions on how to best minimize the impact of certain hazards on their livelihoods. This means accessing both internal community data in real time (the previous paragraph) and data external to the community that bears relevance to the decision-making calculus at the local level. This is why I’m particularly interested in the Open Data for Resilience Initiative (OpenDRI) spearheaded by the World Bank’s Global Facility for Disaster Reduction and Recovery (GFDRR). Institutionalizing OpenDRI at the state level will no doubt be a challenge in and of itself, but I do hope the initiative will also be localized using a people-centered approach like the one described above.

The second way to grow more resilient societies is by enabling them to recover quickly following a disaster. As Manyena wrote in 2006, “increasing attention is now paid to the capacity of disaster-affected communities to ‘bounce back’ or to recover with little or no external assistance following a disaster.” So what factors accelerate recovery in ecosystems in general? “To recover itself, a forest ecosystem needs suitable interactions among climate conditions and bio-actions, and enough area.” In terms of social ecosystems, these interactions can take the form of information exchange.

Identifying needs following a disaster and matching them to available resources is an important part of the process. Accelerating the rate of (1) identification; (2) matching and, (3) allocation, is one way to speed up overall recovery. In ecological terms, how quickly the damaged part of an ecosystem can repair itself depends on how many feedback loops (network connections) it has to the non- (or less-) damaged parts of the ecosystem(s). Some call this an adaptive system. This is where crowdfeeding comes in, as I’ve blogged about here (The Crowd is Always There: A Marketplace for Crowdsourcing Crisis Response) and here (Why Crowdsourcing and Crowdfeeding May be the Answer to Crisis Response).

Internal connectivity and communication is important for crowdfeeding to work, as is preparedness. This is why ICTs are central to growing more resilient societies. They can accelerate the identification of needs, matching and allocation of resources. Free and open source platforms like Ushahidi can also play a role in this respect, as per my recent blog post entitled “Check-In’s With a Purpose: Applications for Disaster Response.” But without sufficient focus on disaster preparedness, these technologies are more likely to facilitate spontaneous response rather than a planned and thus efficient response. As Louis Pas-teur famously noted, “Chance favors the prepared mind.” Hence the rationale for the Standby Volunteer Task Force for Live Mapping (SBTF), for example. Open data is also important in this respect. The OpenDRI initiative is thus important for both damage resistance and quick recovery.

I’m enjoying the process of thinking through these issues again. It’s been a while since I published and presented on the topic of resilience and adaptation. So I plan to read through some of my papers from a while back that addressed these issues in the context of violent conflict and climate change. What I need to do is update them based on what I’ve learned over the past four or five years.

If you’re curious and feel like jumping into some of these papers yourself, I recommend these two as a start:

Meier, Patrick. 2007. “New Strategies for Effective Early Response: Insights from Complexity Science.” Paper prepared for the 48th Annual Convention of the International Studies Association (ISA) in Chicago. Available online.